激光与光电子学进展, 2019, 56 (2): 021503, 网络出版: 2019-08-01   

基于特征融合与子空间学习的行人重识别算法 下载: 1142次

Person Re-Identification Algorithm Based on Feature Fusion and Subspace Learning
朱小波 1,2车进 1,2,*
作者单位
1 宁夏大学物理与电子电气工程学院, 宁夏 银川 750021
2 宁夏大学宁夏沙漠信息智能感知重点实验室, 宁夏 银川 750021
摘要
针对现存行人重识别算法不能较好地适应光照、姿态、遮挡等变化的问题,提出一种基于特征融合与子空间学习的行人重识别算法。该算法对整幅行人图像提取方向梯度(HOG)直方图特征和HSV(Hue,Saturation,Value)直方图特征作为整体特征,再在滑动窗口内提取色彩命名(CN)特征和两个尺度的尺度不变局部三元模式(SILTP)特征。为了使算法具有更好的尺度不变性,对原图像进行两次下采样,再对采样后的图像提取上述特征。提取特征后,采用核函数分别将原始特征空间转换到非线性空间,在非线性空间内学习一个子空间,同时在子空间内学习一个相似性度量函数。在3个公开数据集上进行了实验,结果表明,所提算法可以较好地提高重识别率。
Abstract
Aim

ing at the problem that the existing person re-identification algorithm cannot be adapted well to the variances of illumination, attitude and occlusion, a novel person re-identification algorithm based on feature fusion and subspace learning is proposed, in which the Histogram of Oriented Gradient (HOG) feature and the Hue-Saturation-Value (HSV) histogram feature are first extracted from the entire pedestrian image as the overall feature and then the Color Naming (CN) feature and the two-scale Scale Invariant Local Ternary Pattern (SILTP) feature are extracted in a sliding window. In addition, in order to make this algorithm have better scale invariance, the original images are first down-sampled twice and then the above features are extracted from the sampled images. After the features are extracted, a kernel function is used to transform the original feature space into a nonlinear space, in which a subspace is learned. Simultaneously, in this subspace, a similarity function is learned. The experiments on three public datasets are conducted and the results show that the proposed algorithm can be used to improve the re-identification rate relatively well.

朱小波, 车进. 基于特征融合与子空间学习的行人重识别算法[J]. 激光与光电子学进展, 2019, 56(2): 021503. Xiaobo Zhu, Jin Che. Person Re-Identification Algorithm Based on Feature Fusion and Subspace Learning[J]. Laser & Optoelectronics Progress, 2019, 56(2): 021503.

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